Visual mental imagery abilities in autism
Bibliographic record
Abstract
Abstract In autistic individuals, the role, performance, and autonomy of perceptual functioning are atypical. Overlapping underlying mechanisms of perception and mental imagery predict that the mental imagery abilities of autistic individuals should differ from those of non‐autistic individuals. While enhanced abilities to manipulate mental images have been demonstrated in autism, the other stages of mental imagery (generation, maintenance, inspection) remain to be explored. Forty‐four autistic adults and 42 typical participants performed four tasks to assess different stages of mental imagery: the Image generation task (mentally generating a letter on a grid and indicating whether it passes over a probe located in the grid), the Visual pattern test (maintaining visual patterns in memory), the Image scanning test (inspecting mental images) and the Mental rotation test (mentally manipulating representations of geometric figures). In the image generation task and the mental rotation test, autistic and typical individuals performed equivalently, both in accuracy and response time. The span observed in the visual pattern test was significantly higher in the autistic group, indicating better maintenance of mental images. In the image scanning test, response times were influenced by the distance to mentally inspect in the typical group but not in the autistic group. Autistic participants were equally fast regardless of distance to inspect. Preserved, greater or differently influenced visual mental imagery abilities are in line with an atypical perceptual functioning in autism, possibly reflecting an increased weight of perception‐based information relatively to the top‐down effect of knowledge and language‐based influence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".